Topic-Sensitive Tag Ranking

Social tagging is an increasingly popular way to describe and classify documents on the web. However, the quality of the tags varies considerably since the tags are authored freely. How to rate the tags becomes an important issue. In this paper, we propose a topic-sensitive tag ranking (TSTR) approach to rate the tags on the web. We employ a generative probabilistic model to associate each tag with a distribution of topics. Then we construct a tag graph according to the co-tag relationships and perform a topic-level random walk over the graph to suggest a ranking score for each tag at different topics. Experimental results validate the effectiveness of the proposed tag ranking approach.